A useful customer update should describe what the finished change actually does, why it matters, and whether the customer needs to act—not simply translate a code diff into more words. The practical approach is to give your coding agent a reusable workflow skill that makes it inspect the completed changes before drafting the update, then check the result against the diff.
The template below is a starting point, not a verified copy of the skill implied by the headline or a report of measured customer outcomes. Adapt it to your agent’s skill format and test it on real tasks before relying on it.
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What a customer-facing change skill should do
A skill is reusable workflow guidance, rather than a guarantee that an agent will explain every change correctly. OpenAI describes Codex skills as bundles of instructions, resources, and scripts that help Codex follow workflows and team preferences; they can be requested directly or selected automatically for a task. OpenAI’s Codex app announcement also describes reviewing changes in a thread, commenting on a diff, and opening it in an editor. That makes the resulting diff a practical source of truth for checking an explanation.
For this use case, frame the skill as an encoded team preference: after implementation, inspect the work and present it in a consistent customer-friendly format. Anthropic distinguishes this kind of process guidance from a “capability uplift” skill, intended to improve something a base model cannot do reliably on its own. Unless you have evidence of that kind of improvement, don’t claim the skill makes the model inherently more capable.
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How do I write a coding-agent skill that explains code changes in plain English?
Start with a precise trigger and a short, explicit sequence. The agent should first be able to inspect completed work; then it should identify verified changes, describe their customer relevance without inventing benefits, and state any customer action or limitation. Keep internal deliberation out of the customer-facing text. A community repository offers that last principle as an example of output guidance, but it is not an official Codex or Claude Code rule. See the community repository.
Reusable skill text
# Customer change update
## When to use
Use after implementation work is complete and the resulting changes are available to inspect. Do not use for general coding questions, planning without an implementation, or unrelated requests.
## Workflow
1. Inspect the completed change, such as the diff and relevant test results, before writing.
2. List the meaningful changes that are present. Do not infer a feature, fix, or customer benefit that the change does not support.
3. For each important change, explain in plain language what is different and why a customer might care. Preserve meaningful limitations and behavior changes.
4. State any action the customer needs to take. If none is apparent from the change, say that no action is indicated by the available information; do not guess.
5. Keep the update concise and focused on customer impact. Omit implementation detail unless it helps the customer understand the change.
6. If the diff does not establish the effect, or you cannot inspect the change, say what is unknown rather than presenting an assumption as fact.
## Output
- What changed: [verified customer-facing changes]
- Why it matters: [supported customer relevance]
- What you need to do: [required action, or no action indicated]
- Limitations or follow-up: [only when supported; otherwise omit]
Do not include private deliberation, speculative benefits, or claims unsupported by the inspected changes.This is deliberately platform-neutral example text, not a claim that one file location or format works across every coding agent. Place it where your agent discovers reusable instructions, and follow that product’s documented conventions. In Codex, skills can be explicitly requested or selected automatically; choose the invocation style deliberately. A manual invocation is easier to control while you tune the workflow, while automatic selection depends on a description that distinguishes implementation-completion tasks from unrelated work.
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How do I make my coding agent explain every change to the customer?
“Every change” should mean every meaningful customer-relevant change, not every modified line or internal refactor. Before sharing an update, compare each claim with the actual diff. Remove claims that cannot be verified, and retain behavior changes, limitations, or required customer actions that would affect how someone uses the product.
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- Generate the update from the change. Invoke the skill after implementation, not from a vague task description that may omit what was actually completed.
- Review claims against the diff. In Codex, the product announcement describes reviewing changes in a thread, commenting on the diff, and opening it in an editor. Use the review surface available in your agent to verify each statement.
- Edit before sending. Correct unsupported benefits, missing limitations, unexplained jargon, or actions that the customer does not actually need to take.
Do not imply that a feature is faster, safer, or easier unless the code and available evidence support that specific claim. The skill should translate verified behavior, not turn implementation intent into a promise.
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How to evaluate accuracy, readability, and triggering
Evaluate the explanation itself separately from whether the skill runs on the right tasks. Anthropic’s March 3, 2026 article on improving skill creation describes writing evals, benchmarking, iterative refinement, and tuning skill descriptions to reduce false triggers or missed triggers. It calls a SKILL.md file “essentially an implementation plan” and says its skill-creator helps teams “write evals, run benchmarks, and keep your skills working as models evolve.” Those are Anthropic’s descriptions of its own skill-authoring approach, not guarantees about other platforms.
Check the output
- Fidelity: Does every stated change exist in the inspected work?
- Coverage: Are important customer-visible effects, limitations, and next steps included?
- Comprehension: Could a customer understand the wording without knowing the implementation jargon?
- Restraint: Does the update avoid speculative benefits and unnecessary implementation detail?
Check when it runs
- Use positive examples where implementation is complete and a customer update is appropriate.
- Use negative examples such as planning-only requests, general coding questions, and unrelated tasks where a customer update should not be produced.
- Track missed triggers as well as false triggers; a description that is too narrow can prevent the skill from being selected, while one that is too broad can select it unnecessarily.
For a meaningful comparison, run representative tasks both with the skill and without it, or compare two skill versions. Review outputs against the same checks rather than judging only whether one sounds polished. Repeat the evaluation after changing the skill or the underlying agent, because model changes can affect triggering and output quality.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available evidence does—and does not—show
OpenAI’s 2026 Codex app announcement reports using image-generation and web-game-development skills to build an example game from one initial prompt, consuming more than 7 million tokens. That is a vendor-reported illustration about a different task, not evidence that a customer-explanation skill improves customer understanding. The sources cited here do not establish a measured improvement in comprehension, a tested version of the template above, or compatibility across coding-agent platforms. Treat the template as a workflow to validate in your own environment.
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